Currently, Lyme disease is growing in both number of cases and geographic distribution in the U.S. Because no vaccines currently are available for public distribution, prevention of Lyme disease has involved educating the public to reduce risk of exposure to tick bites. The proposed research will provide a foundation for further efforts at public education by increasing our ability to predict when and where infected ticks will be most abundant. This research involves new initiatives to understand relationships between tick hosts, ticks, and spirochetes, that strongly influence Lyme disease risk. We propose to test a series of specific hypotheses regarding the ecological determinants of distribution and Borrelia-infection rates of I. scapularis in a semirural area in Dutchess County, New York. We will expand upon ongoing studies of resource (including acorn) abundance, small mammal abundance and diversity, abundance and distribution of host-seeking and attached ticks, and tick infection rates in five distinct habitat types representing landscapes typical of the northeastern and upper midwestern U.S. We will conduct replicated acorn removal (in mast years) and acorn addition (in nonmast years) experiments to test the hypothesis that, in forests, acorn abundance in autumn determines density of larval ticks the following summer. To test the hypothesis that nymphal tick abundance and infection rates are determined by the community structure of vertebrate hosts, we will live trap and mist net virtually all mammalian and avian hosts within several habitat types. The habitat types are expected to differ in host community structure. By collecting fed ticks from a complete array of potential hosts, we will be able to relate the proportion of larvae feeding on reservoir-competent hosts to the infection rate of nymphs and adults the following year. We will also test the hypothesis that density-dependent dispersal by P. leucopus causes predictable shifts in the habitat distribution of ticks between the larval and nymphal stages. This will be accomplished by enlarging current trapping grids to encompass patch boundaries in order to capture and examine dispersing mice. Our data will be instrumental in devising host- management strategies for controlling Lyme disease. Our ultimate goal is to forecast in which habitat types and in which years humans will be at the greatest risk of exposure to bites from infected ticks, and hence to contracting Lyme disease. The ability to forecast Lyme disease risk will require an understanding of the ecological interactions between resources, tick hosts, and ticks. We expect three more years of study in our system will establish sufficient ecological knowledge to allow forecasting with high probability. This knowledge will be disseminated to public health professionals and others important in public education regarding Lyme disease. Moreover, we will test our model's prediction that high species diversity in the host community will reduce or eliminate risk of Lyme disease; these results can be applied to environmentally sound host-management. Our data for Dutchess County, NY, will be used to evaluate the emergence of the enzootic cycle in nonendemic areas surrounding our region.